SoroushVahidi/scidocs huggingface dataset:
An Error-Oriented Approach to Word Embedding Pre-Training
(2017)

We propose a novel word embedding pretraining approach that exploits writing errors in learners’ scripts. We compare our method to previous models that tune the embeddings based on script scores and the discrimination between correct and corrupt word contexts in addition to the generic commonly-used embeddings pre-trained on large corpora. The comparison is achieved by using the aforementioned models to bootstrap a neural network that learns to predict a holistic score for scripts. Furthermore, we investigate augmenting our model with error corrections and monitor the impact on performance. Our results show that our error-oriented approach outperforms other comparable ones which is further demonstrated when training on more data. Additionally, extending the model with corrections provides further performance gains when data sparsity is an issue.
Single-Channel and Multi-Channel Sinusoidal Audio Coding Using Compressed Sensing
(2011)

Compressed sensing (CS) samples signals at a much lower rate than the Nyquist rate if they are sparse in some basis. In this paper, the CS methodology is applied to sinusoidally modeled audio signals. As this model is sparse by definition in the frequency domain (being equal to the sum of a small number of sinusoids), we investigate whether CS can be used to encode audio signals at low bitrates. In contrast to encoding the sinusoidal parameters (amplitude, frequency, phase) as current state-of-the-art methods do, we propose encoding few randomly selected samples of the time-domain description of the sinusoidal component (per signal segment). The potential of applying compressed sensing both to single-channel and multi-channel audio coding is examined. The listening test results are encouraging, indicating that the proposed approach can achieve comparable performance to that of state-of-the-art methods. Given that CS can lead to novel coding systems where the sampling and compression operations are combined into one low-complexity step, the proposed methodology can be considered as an important step towards applying the CS framework to audio coding applications.
Knowledge Discovery in Biomedical Data Facilitated by Domain Ontologies
(2007)

In some real-world areas, it is important to enrich the data with external background knowledge so as to provide context and to facilitate pattern recognition. These areas may be described as data rich but knowledge poor. There are two challenges to incorporate this biological knowledge into the data mining cycle: (1) generating the ontologies; and (2) adapting the data mining algorithms to make use of the ontologies. This chapter presents the state-of-the-art in bringing the background ontology knowledge into the pattern recognition task for biomedical data.
AUTOMATIC GENERATION OF PRESENTATION SLIDES FOR ACADEMIC PAPERS USING INTEGER LINEAR PROGRAMMING
(2017)

Presentations are one of the most common and effective ways of communicating the overview of a work to the audience. Given a specialized paper, programmed era of introduction slides diminishes the exertion of the moderator and aides in making an organized synopsis of the paper. In this Project, we propose the structure of another framework that does this assignment. Any paper that has a theoretical and whose segments can be ordered under presentation, related work, model, examinations and conclusions can be given as info. This XML record is parsed and data in it is extricated. An inquiry particular extractive summarizer has been utilized to create slides. In numerous meetings and social occasions, a moderator takes the guide of slides to present his work deliberately (pictorial). The introduction slides for the paper are then made by utilizing the Integer Linear Programming (ILP) demonstrate with complicatedly organized target breaking points and goals to pick and adjust key expressions and sentences. The framework removed theme and non subject parts from the article in light of the basic talk structure. The isolated subject and non-topic parts were determined to the slides by giving fitting indents in perspective of the examination of their syntactic structure. Keywords— Abstracting methods, Integer Linear Programming, Support Vector Regression model, text mining.
Semi-Supervised Text Classification Using EM
(2006)

For several decades, statisticians have advocated using a c ombination of labeled and unlabeled data to train classifiers by estimating parameters o f a generative model through iterative Expectation-Maximization (EM) techniques. Thi s chapter explores the effectiveness of this approach when applied to the domain of text class ification. Text documents are represented here with a bag-of-words model, which leads to a generative classification model based on a mixture of multinomials. This model is an ext remely simplistic representation of the complexities of written text. This chapter explains and illustrates three key points about semi-supervised learning for text classifi cat on with generative models. First, despite the simplistic representation, some text do mains have a high positive correlation between generative model probability and classifica tion accuracy. In these domains, a straightforward application of EM with the naive Bayes tex t model works well. Second, some text domains do not have this correlation. Here we can ad opt a more expressive and appropriate generative model that does have a positive c orrelation. In these domains, semi-supervised learning again improves classification ac curacy. Finally, EM suffers from the problem of local maxima, especially in high dimension do mains such as text classification. We demonstrate that deterministic annealing, a varia nt of EM, can help overcome the problem of local maxima and increase classification accurac y further when the generative model is appropriate.
Crown and post-free adhesive restorations for endodontically treated posterior teeth: from direct composite to endocrowns.
(2013)

Coronal rehabilitation of endodontically treated posterior teeth is still a controversial issue. Although the classical crown supported by radicular metal posts remains widely spread in dentistry, its invasiveness has been largely criticized. New materials and therapeutic options based entirely on adhesion are nowadays available. They allow performing a more conservative, faster and less expensive dental treatment. All clinical cases presented in this paper are solved by using these modern techniques, from direct composite restorations to indirect endocrowns.
Advanced Safe PIN-Entry Against Human Shoulder-Surfing Ms
(2015)

When users insert their passwords in a common area, they might be at risk of aggressor stealing their password. The PIN entry can be perceived by close by adversaries, more effectually in a crowded place. A new technique has been established to cope with this problem that is cryptography prevention techniques. Instead, there have been alternative approaches among them, the PIN entry was elegant because of its simplicity and accessibility. The basic BW method is focused to withstand a human shoulder surfing attack. In every round, a well ordered numeric keypad is colored at odd. A user who knows the accurate PIN digit can enter by pressing the separate color key. The IBW method is examined to be confidential against human nemesis due to the restricted cognitive abilities of humans. Also the IBW method is proven to be robust against any hacking attacks.
A recommender system using GA K-means clustering in an online shopping market
(2008)

The Internet is emerging as a new marketing channel, so understanding the characteristics of online customers’ needs and expectations is considered a prerequisite for activating the consumer-oriented electronic commerce market. In this study, we propose a novel clustering algorithm based on genetic algorithms (GAs) to effectively segment the online shopping market. In general, GAs are believed to be effective on NP-complete global optimization problems, and they can provide good near-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters more effectively. The research in this paper applied K-means clustering whose initial seeds are optimized by GA, which is called GA K-means, to a real-world online shopping market segmentation case. In this study, we compared the results of GA K-means to those of a simple K-means algorithm and self-organizing maps (SOM). The results showed that GA K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms. In addition, our study validated the usefulness of the proposed model as a preprocessing tool for recommendation systems. 2007 Elsevier Ltd. All rights reserved.
Large-scale deep learning at Baidu
(2013)

In the past 30 years, tremendous progress has been achieved in building effective shallow classification models. Despite the success, we come to realize that, for many applications, the key bottleneck is not the qualify of classifiers but that of features. Not being able to automatically get useful features has become the main limitation for shallow models. Since 2006, learning high-level features using deep architectures from raw data has become a huge wave of new learning paradigms. In recent two years, deep learning has made many performance breakthroughs, for example, in the areas of image understanding and speech recognition. In this talk, I will walk through some of the latest technology advances of deep learning within Baidu, and discuss the main challenges, e.g., developing effective models for various applications, and scaling up the model training using many GPUs. In the end of the talk I will discuss what might be interesting future directions.
SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging
(2017)

Convolutional Neural Networks (CNNs) are typically trained in the RGB color space. However, in medical imaging, we believe that pixel stain quantities offer a fundamental view of the interaction between tissues and stain chemicals. Since the optical density (OD) colorspace allows to compute pixel stain quantities from pixel RGB intensities using the Beer-Lambert’s law, we propose a stain deconvolutional layer, hereby named as SD-Layer, affixed at the front of CNN that performs two functions: (1) it transforms the input RGB microscopic images to Optical Density (OD) space and (2) this layer deconvolves OD image with the stain basis learned through backpropagation and provides tissue-specific stain absorption quantities as input to the following CNN layers. With the introduction of only nine additional learnable parameters in the proposed SD-Layer, we obtain a considerably improved performance on two standard CNN architectures: AlexNet and T-CNN. Using the T-CNN architecture prefixed with the proposed SD-Layer, we obtain 5-fold crossvalidation accuracy of 93.2% in the problem of differentiating malignant immature White Blood Cells (WBCs) from normal immature WBCs for cancer detection.
Principles of spatial database analysis and design
(1999)

This chapter covers the fundamentals of spatial database analysis and design, It begins by defining the most important concepts: ‘spatial database’, ‘analysis’, ‘design’, and ‘model’; and continues with a presentation of the rationale supporting the use of formal methods for analysis and design. The basic elements and approaches of such methods are described, in addition to the processes used. Emphasis is placed on the particularities of spatial databases and the improvements needed for non-spatial methods and tools in order to enhance their efficiency. Finally, the chapter presents a set of tools, called CASE (computer-assisted software engineering), which are built to support the formal analysis and design methods.
A BiCMOS Ultra-Wideband 3.1&#8211;10.6-GHz Front-End
(2006)

This paper presents a direct-conversion receiver for FCC-compliant ultra-wideband (UWB) Gaussian-shaped pulses that are transmitted in one of fourteen 500-MHz-wide channels within the 3.1-10.6-GHz band. The receiver is fabricated in 0.18-mum SiGe BiCMOS. The packaged chip consists of an unmatched wideband low-noise amplifier (LNA), filter, phase-splitter, 5-GHz ISM band switchable notch filter, 3.1-10.6-GHz local oscillator (LO) amplifiers, mixers, and baseband channel-select filters/buffers. The required quadrature single-ended LO signals are generated externally. The average conversion gain and input P1dB are 32 dB and -41 dBm, respectively. The unmatched LNA provides a system noise figure of 3.3 to 5 dB over the entire band. The chip draws 30 mA from 1.8 V. To verify the unmatched LNA's performance in a complete system, wireless testing of the front-end embedded in a full receiver at 100 Mbps reveals a 10-3 bit-error rate (BER) at -80 dBm sensitivity. The notch filter suppresses out-of-band interferers and reduces the effects of intermodulation products that appear in the baseband. BER improvements of an order of magnitude and greater are demonstrated with the filter
Information-theoretical label embeddings for large-scale image classification
(2016)

We present a method for training multi-label, massively multi-class image classification models, that is faster and more accurate than supervision via a sigmoid cross-entropy loss (logistic regression). Our method consists in embedding high-dimensional sparse labels onto a lower-dimensional dense sphere of unit-normed vectors, and treating the classification problem as a cosine proximity regression problem on this sphere. We test our method on a dataset of 300 million high-resolution images with 17,000 labels, where it yields considerably faster convergence, as well as a 7% higher mean average precision compared to logistic regression.
Analysis of a microstrip patch array fed cylindric lens antenna for 77GHz automotive radar
(2008)

Regarding gain and SLL, the new lens antenna concept has better focusing abilities compared to the column antenna. Pattern perturbations due to lens edge effects were analyzed with regard to digital array processing. They can be confined either with an appropriate choice for the lens length L and the xlscr,i of the radar sensorpsilas receiving ULA or with narrower feeding beams in the H-plane.
Towards Modelling Pedestrian-Vehicle Interactions: Empirical Study on Urban Unsignalized Intersection
(2016)

The modelling and simulation of the interaction among vehicles and pedestrians during crosswalking is an open challenge for both research and practical computational solutions supporting urban/traffic decision makers and managers. The social cost of pedestrians’ risky behaviour pushes the development of a new generation of computational models integrating analytical knowledge, data and experience about the complex dynamics occurring in pedestrian/vehicle interactions, which are not completely understood despite recent efforts. This paper presents the results of a significant data gathering campaign realised at an unsignalized zebra crossing. The selected area of the city of Milan (Italy) is characterised by a significant presence of elderly inhabitants and pedestrian-vehicle risky interactions, testified by a high number of accidents involving pedestrians in the past years. The results concern the analysis of: (i) vehicular and pedestrian traffic volumes; (ii) level of service; (iii) pedestrian-vehicle interactions, considering the impact of ageing on crossing behaviour. Results showed that the phenomenon is characterised by three main phases: approaching, appraising (evaluation of the distance and speed of oncoming vehicles) and crossing. The final objective of the research is to support the development of a microscopic agent-based tool for simulating pedestrian behaviour at unsignalized crosswalks, focusing on the specific needs of the elderly pedestrians.
Status of market, regulation and research of genetically modified crops in Chile.
(2016)

Agricultural biotechnology and genetically modified (GM) crops are effective tools to substantially increase productivity, quality, and environmental sustainability in agricultural farming. Furthermore, they may contribute to improving the nutritional content of crops, addressing needs related to public health. Chile has become one of the most important global players for GM seed production for counter-season markets and research purposes. It has a comprehensive regulatory framework to carry out this activity, while at the same time there are numerous regulations from different agencies addressing several aspects related to GM crops. Despite imports of GM food/feed or ingredients for the food industry being allowed without restrictions, Chilean farmers are not using GM seeds for farming purposes because of a lack of clear guidelines. Chile is in a rather contradictory situation about GM crops. The country has invested considerable resources to fund research and development on GM crops, but the lack of clarity in the current regulatory situation precludes the use of such research to develop new products for Chilean farmers. Meanwhile, a larger scientific capacity regarding GM crop research continues to build up in the country. The present study maps and analyses the current regulatory environment for research and production of GM crops in Chile, providing an updated overview of the current status of GM seeds production, research and regulatory issues.
A Machine Learning Approach to Software Requirements Prioritization
(2013)

Deciding which, among a set of requirements, are to be considered first and in which order is a strategic process in software development. This task is commonly referred to as requirements prioritization. This paper describes a requirements prioritization method called Case-Based Ranking (CBRank), which combines project's stakeholders preferences with requirements ordering approximations computed through machine learning techniques, bringing promising advantages. First, the human effort to input preference information can be reduced, while preserving the accuracy of the final ranking estimates. Second, domain knowledge encoded as partial order relations defined over the requirement attributes can be exploited, thus supporting an adaptive elicitation process. The techniques CBRank rests on and the associated prioritization process are detailed. Empirical evaluations of properties of CBRank are performed on simulated data and compared with a state-of-the-art prioritization method, providing evidence of the method ability to support the management of the tradeoff between elicitation effort and ranking accuracy and to exploit domain knowledge. A case study on a real software project complements these experimental measurements. Finally, a positioning of CBRank with respect to state-of-the-art requirements prioritization methods is proposed, together with a discussion of benefits and limits of the method.
Novel Feed Network for Circular Polarization Antenna Diversity
(2014)

In this letter, we propose a novel feed network for circular polarization (CP) diversity antennas to enhance communications performances. The previous CP diversity systems adopted dual polarization diversity using right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) within a small space. However, the same rotational CPs need further antenna distance. To overcome the space limitation for the same CP diversity antenna, we propose a feed network for CP antenna using conventional CP with orthogonal polarization radiation characteristics of a linear polarization (LP) diversity system without additional antennas. The port isolation characteristics and impedance matching conditions are numerically verified for the diversity system and antenna characteristics. The proposed feed network with CP patch antennas is fabricated and measured in a reverberation chamber for diversity performances, and their validity is verified.
Microcephaly and early-onset nephrotic syndrome —confusion in Galloway-Mowat syndrome
(1995)

We report a 2-year-old girl with nephrotic syndrome, microcephaly, seizures and psychomotor retardation. Histological studies of a renal biopsy revealed focal glomerular sclerosis with mesangiolysis and capillary microaneurysms. Dysmorphic features were remarkable: abnormal-shaped skull, coarse hair, narrow forehead, large low-set ears, almond-shaped eyes, low nasal bridge, pinched nose, thin lips and micrognathia. Cases with this rare combination of microcephaly and early onset of nephrotic syndrome with various neurological abnormalities have been reported. However, clinical manifestations and histological findings showed a wide variation, and there is a lot of confusion in this syndrome. We therefore reviewed the previous reports and propose a new clasification of this syndrome.
Learning Parameterized Models of Image Motion
(1997)

A f?umeworkfor learning parumeterized models oj‘optical flow from image sequences is presented. A class of motions is represented by U set of orthogonul basis $ow fields that are computed froni a training set using priiicipal component analysis. Many complex image motions can be represeiited by a linear combiriution of U small number of these basisjows. The learned motion models may be usedfilr optical flow estimation und for model-bused recognition. For optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. As examples we consider learning motion discontinuities, non-rigid motion of human mouths, und urticulated human motion.
Teaching cryptography with open-source software
(2008)

Cryptography has become an important topic in undergraduate curricula in mathematics and computer science, not just for its intrinsic interest---``about the most fun you can have with mathematics''\cite{ferg04, but for its current standing as the basis for almost all computer security. From wireless networking to secure email to password protection, cryptographic methods are used to secure information, to protect users, and to protect data.
 At Victoria University, cryptography has been taught as part of a mathematics and computer science degree for several years. The students all have had at least a year of tertiary mathematics, and some exposure to a computer algebra system (Maple). However, the cost of Maple, and the current licensing agreement, means that students are unable to experiment with the software away from the computer laboratories at the University. For this reason we have decided to investigate the use of open-source computer algebra systems Maxima and Axiom. Although not as full-featured and powerful as the commercial systems Maple and Mathematica, we show they are in fact admirably suited for a subject such as cryptography. In some ways Maxima and Axiom even surpass Maple and Mathematica. Student response to the introduction of these systems has been very positive.
Web-based models for natural language processing
(2005)

Previous work demonstrated that Web counts can be used to approximate bigram counts, suggesting that Web-based frequencies should be useful for a wide variety of Natural Language Processing (NLP) tasks. However, only a limited number of tasks have so far been tested using Web-scale data sets. The present article overcomes this limitation by systematically investigating the performance of Web-based models for several NLP tasks, covering both syntax and semantics, both generation and analysis, and a wider range of n-grams and parts of speech than have been previously explored. For the majority of our tasks, we find that simple, unsupervised models perform better when n-gram counts are obtained from the Web rather than from a large corpus. In some cases, performance can be improved further by using backoff or interpolation techniques that combine Web counts and corpus counts. However, unsupervised Web-based models generally fail to outperform supervised state-of-the-art models trained on smaller corpora. We argue that Web-based models should therefore be used as a baseline for, rather than an alternative to, standard supervised models.